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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xuelian Long Joshi, J. |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Information Sciences, University of Pittsburgh, PA 15213 USA (Xuelian Long; Joshi, J.) |
| Abstract | Current distributed applications offer a variety of flexible and convenient services through the Internet to users from different geographic locations and also support communications among them. However, security and trust are key concerns in such applications as users in such an environment are unknown to each other. Trust management systems aim to decrease the risks in such applications and protect benign users from malicious users. In this paper, we propose a novel Bayesian Reputation Management System (BaRMS) for Peer-to-Peer (P2P) environments using Bayesian probability and Markov Chain theories. BaRMS handles negative feedbacks and through a case study, we show that this approach is better than the existing EigenTrust framework for P2P systems. Moreover, our simulation results of a P2P file sharing system also show that the proposed algorithm can greatly improve the performance over a system that does not include a trust management service. We show that our proposed Bayesian Reputation Computation Algorithm (BaRCA) performs better than the EigenTrust algorithm. |
| Starting Page | 147 |
| Ending Page | 152 |
| File Size | 389946 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424480975 |
| e-ISBN | 9781424480999 |
| DOI | 10.1109/IRI.2010.5558948 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-04 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Negative feedback Markov processes Generators Prediction algorithms Computer architecture Computational modeling Markov Chain Reputation Management Bayesian Model |
| Content Type | Text |
| Resource Type | Article |
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